Our faculty are affiliated with institutes and centers across the Cleveland area, and have a wide range of CWRU resources available for research and education.

Prevention Research Center for Healthy Neighborhoods

The PRCHN fosters partnerships within Cleveland’s urban neighborhoods for developing, testing, and implementing strategies to prevent and reduce the burden of chronic disease.See more information >

Institute for Computational Biology

The ICB advances our fundamental knowledge of human biology through the application of computational methods to large and diverse datasets, and promotes the translation of this knowledge into better diagnosis, prognosis, treatment, prevention and delivery of healthcare.See more information >

Clinical and Translational Science Collaborative

The CTSC of Cleveland provides developmental, organizational, financial, and educational support to biomedical researchers as well as opportunities for community members to participate in meaningful and valuable research.Learn more >

The Case Comprehensive Cancer Center

The Case Comprehensive Cancer Center (Case CCC) based at Case Western Reserve University (CWRU) is a partnership organization supporting cancer-related research efforts at CWRU, University Hospitals Cleveland Medical Center, and Cleveland Clinic.Find out more >

Many clinical trials entail multiple endpoints, reflecting diverse aspects of treatment
effects, and yielding challenging research topics. Traditional approaches analyze
endpoints individually, leading to increased type I error or larger required sample sizes if
adjustment is made for multiple testing. In addition, small-to-mid-size trials are often
underpowered on clinical outcomes when the event rate is low. Multivariate hierarchical
global rank test, a nonparametric approach that incorporates same or different types of
endpoints into a hierarchical composite endpoint based on relative clinical importance and
tests research hypotheses using ranking strategies, has been proposed as a plausible
alternative adding to traditional approaches. Various global rank test approaches, with
nice applications in the clinical setting, have been published. However, there is still room
to improve the precision of global ranking test. In this research we will (1) compare the
performance of global rank tests, in a variety of aspects such as Type-I error, statistical
power, bias of test statistic estimates, and coverage probabilities of confidence intervals;
(2) develop a stratified global rank test strategy; (3) apply the global rank test methods to
the AQUARIUS trial data set; and (4) optionally, compare the power of the nonparametric
global rank test to a test based on parametric joint modeling.